All charts

## Load libraries
library(covid19)
library(ggplot2)
library(lubridate)
library(dplyr)
library(ggplot2)
library(sp)
library(raster)
library(viridis)
library(ggthemes)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(RColorBrewer)
library(readr)
library(zoo)
library(tidyr)
options(scipen = '999')

Daily cases Spain

pd <- df_country %>%
  filter(country == 'Spain')

ggplot(data = pd,
       aes(x = date,
           y = cases_non_cum)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Casos diarios',
       title = 'Casos confirmados nuevos')

Daily deahts in Spain

pd <- df_country %>%
  filter(country == 'Spain')

ggplot(data = pd,
       aes(x = date,
           y = deaths_non_cum)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Muertes diarias',
       title = 'Muertes')

Daily cases world / population-adjusted

covid19::plot_day_zero(countries = c('Italy', 'Spain', 'US', 'Germany',
                                     'Canada', 'UK', 'Netherlands'
                                     ),
                       ylog = F,
                       day0 = 1,
                       cumulative = F,
                       calendar = T,
                       pop = T,
                       point_alpha = 0,
                       color_var = 'geo')

Cases per pop last week

pd <- df_country %>%
  left_join(world_pop %>% dplyr::select(iso, pop)) %>%
  group_by(country) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(country) %>%
  summarise(y = sum(cases_non_cum),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date)),
            yp = sum(cases_non_cum) / dplyr::first(pop) * 1000000) %>%
  ungroup %>%
  filter(pop > 1000000) %>%
  arrange(desc(yp)) %>%
  head(15) %>%
  mutate(country = ifelse(country == 'United Kingdom', 'UK', country))
pd$country <- factor(pd$country, levels = unique(pd$country))

ggplot(data = pd,
       aes(x = country,
           y = yp)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12)) +
  geom_text(aes(label = round(yp, digits = 0)),
            nudge_y = -50,
            color = 'white') +
  labs(x = '',
       y = 'Cases per 1,000,000 (last 7 days)',
       title = 'New confirmed COVID-19 cases per million population, last 7 days')

Deaths per pop last week

pd <- df_country %>%
  left_join(world_pop %>% dplyr::select(iso, pop)) %>%
  group_by(country) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(country) %>%
  summarise(y = sum(deaths_non_cum),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date)),
            yp = sum(deaths_non_cum) / dplyr::first(pop) * 1000000) %>%
  ungroup %>%
  filter(pop > 1000000) %>%
  arrange(desc(yp)) %>%
  head(10) %>%
  mutate(country = ifelse(country == 'United Kingdom', 'UK', country))
pd$country <- factor(pd$country, levels = unique(pd$country))

ggplot(data = pd,
       aes(x = country,
           y = yp)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12)) +
  geom_text(aes(label = round(yp, digits = 0)),
            nudge_y = -10,
            color = 'white') +
  labs(x = '',
       y = 'Deaths per 1,000,000 (last 7 days)',
       title = 'New confirmed COVID-19 deaths per million population, last 7 days')

Lombardia, Catalonia, Madrid

New cases in last week

pd <- esp_df %>%
  left_join(esp_pop %>% dplyr::select(ccaa, pop)) %>%
  mutate(country = 'Spain') %>%
  bind_rows(
    ita %>% left_join(ita_pop %>% dplyr::select(ccaa, pop)) %>% mutate(country = 'Italy')
  ) %>%
  bind_rows(
    df %>% filter(country == 'US') %>% mutate(ccaa = district) %>% left_join(regions_pop %>% dplyr::select(ccaa, pop)) %>% mutate(country = 'US')) %>%
  group_by(ccaa) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(ccaa) %>%
  summarise(y = sum(cases_non_cum),
            country = dplyr::first(country),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date))) %>%
  ungroup %>%
  mutate(yp = y / pop * 1000000) %>%
  ungroup %>%
  # filter(pop > 1000000) %>%
  arrange(desc(yp)) 

#Get country totals
pd %>%
  group_by(country) %>%
  summarise(y = sum(y, na.rm = T),
            pop = sum(pop, na.rm = T)) %>%
  ungroup %>%
  mutate(yp = y / pop * 1000000)

# A tibble: 3 x 4
  country      y       pop    yp
  <chr>    <dbl>     <dbl> <dbl>
1 Italy    27415  60491453  453.
2 Spain    34464  47026208  733.
3 US      218254 328239523  665.

library(knitr)
library(kableExtra)
pd <- pd %>%
  mutate(yp = round(yp, digits = 1)) %>%
  mutate(Rank = 1:nrow(pd)) %>%
  dplyr::select(Rank, 
                Región = ccaa,
                `Casos nuevos, 7 días` = y,
                Población = pop,
                `Casos nuevos 7 días por millón` = yp)
kable(pd) %>%
  kable_styling("striped", full_width = F) %>%
  column_spec(which(names(pd) == 'Casos nuevos 7 días por millón'), bold = T) %>%
  row_spec(which(pd$`Región` %in% esp_df$ccaa), bold = T, color = "white", background = "#D7261E")

Rank	Región	Casos nuevos, 7 días	Población	Casos nuevos 7 días por millón
1	New York	65873	19453561	3386.2
2	New Jersey	24345	8882190	2740.9
3	La Rioja	639	316798	2017.1
4	Massachusetts	12975	6892503	1882.5
5	Connecticut	6360	3565287	1783.9
6	CLM	3452	2032863	1698.1
7	Rhode Island	1743	1059361	1645.3
8	Louisiana	7585	4648794	1631.6
9	CyL	3512	2399548	1463.6
10	Navarra	861	654214	1316.1
11	Madrid	8423	6663394	1264.1
12	District of Columbia	873	705749	1237.0
13	Trentino-Alto Adige	1222	1070000	1142.1
14	Valle d’Aosta	139	126202	1101.4
15	Cataluña	7902	7675217	1029.5
16	País Vasco	2208	2207776	1000.1
17	Piemonte	4298	4376000	982.2
18	Delaware	952	973764	977.6
19	Pennsylvania	11349	12801989	886.5
20	Lombardia	8597	10040000	856.3
21	Michigan	8526	9986857	853.7
22	Maryland	4608	6045680	762.2
23	Illinois	9593	12671821	757.0
24	Emilia-Romagna	3009	4453000	675.7
25	Liguria	1045	1557000	671.2
26	Aragón	840	1319291	636.7
27	Veneto	2851	4905000	581.2
28	South Dakota	490	884659	553.9
29	Extremadura	590	1067710	552.6
30	Marche	839	1532000	547.7
31	Georgia	5805	10617423	546.7
32	Indiana	3517	6732219	522.4
33	Cantabria	294	581078	506.0
34	Galicia	1343	2699499	497.5
35	Colorado	2357	5758736	409.3
36	Mississippi	1143	2976149	384.1
37	Toscana	1388	3737000	371.4
38	Alabama	1798	4903185	366.7
39	Washington	2784	7614893	365.6
40	Florida	7545	21477737	351.3
41	Abruzzo	457	1315000	347.5
42	C. Valenciana	1726	5003769	344.9
43	Vermont	215	623989	344.6
44	Nevada	981	3080156	318.5
45	Friuli Venezia Giulia	383	1216000	315.0
46	Missouri	1922	6137428	313.2
47	Virginia	2634	8535519	308.6
48	Asturias	312	1022800	305.0
49	Tennessee	1875	6829174	274.6
50	New Mexico	575	2096829	274.2
51	South Carolina	1271	5148714	246.9
52	Iowa	718	3155070	227.6
53	New Hampshire	308	1359711	226.5
54	Kentucky	1008	4467673	225.6
55	Texas	6468	28995881	223.1
56	Nebraska	427	1934408	220.7
57	Ohio	2561	11689100	219.1
58	Utah	695	3205958	216.8
59	Kansas	593	2913314	203.5
60	Baleares	230	1149460	200.1
61	California	7761	39512223	196.4
62	Andalucía	1606	8414240	190.9
63	Idaho	329	1787065	184.1
64	North Carolina	1921	10488084	183.2
65	Oklahoma	716	3956971	180.9
66	Wisconsin	1021	5822434	175.4
67	Melilla	15	86487	173.4
68	Puglia	672	4048000	166.0
69	Lazio	965	5897000	163.6
70	West Virginia	269	1792147	150.1
71	Arkansas	443	3017804	146.8
72	Arizona	1056	7278717	145.1
73	Ceuta	12	84777	141.5
74	Canarias	295	2153389	137.0
75	Murcia	204	1493898	136.6
76	North Dakota	101	762062	132.5
77	Wyoming	73	578759	126.1
78	Sardegna	206	1648000	125.0
79	Minnesota	686	5639632	121.6
80	Maine	163	1344212	121.3
81	Alaska	87	731545	118.9
82	Campania	644	5827000	110.5
83	Oregon	459	4217737	108.8
84	Molise	33	308493	107.0
85	Montana	101	1068778	94.5
86	Umbria	80	884640	90.4
87	Hawaii	128	1415872	90.4
88	Sicilia	422	5027000	83.9
89	Calabria	128	1957000	65.4
90	Basilicata	37	567118	65.2
91	American Samoa	0	NA	NA
92	Diamond Princess	0	NA	NA
93	Grand Princess	0	NA	NA
94	Guam	21	NA	NA
95	Northern Mariana Islands	3	NA	NA
96	Puerto Rico	422	NA	NA
97	Recovered	0	NA	NA
98	United States Virgin Islands	6	NA	NA
99	US	1	NA	NA
100	Virgin Islands	9	NA	NA
101	Wuhan Evacuee	4	NA	NA
102	NA	2	NA	NA

Just Italy and Spain

xpd = pd %>% filter(`Región` %in% c(ita$ccaa, esp_df$ccaa))
xpd$Rank <- 1:nrow(xpd)

kable(xpd) %>%
  kable_styling("striped", full_width = F) %>%
  column_spec(which(names(xpd) == 'Casos nuevos 7 días por millón'), bold = T) %>%
  row_spec(which(xpd$`Región` %in% esp_df$ccaa), bold = T, color = "white", background = "#D7261E")

Rank	Región	Casos nuevos, 7 días	Población	Casos nuevos 7 días por millón
1	La Rioja	639	316798	2017.1
2	CLM	3452	2032863	1698.1
3	CyL	3512	2399548	1463.6
4	Navarra	861	654214	1316.1
5	Madrid	8423	6663394	1264.1
6	Trentino-Alto Adige	1222	1070000	1142.1
7	Valle d’Aosta	139	126202	1101.4
8	Cataluña	7902	7675217	1029.5
9	País Vasco	2208	2207776	1000.1
10	Piemonte	4298	4376000	982.2
11	Lombardia	8597	10040000	856.3
12	Emilia-Romagna	3009	4453000	675.7
13	Liguria	1045	1557000	671.2
14	Aragón	840	1319291	636.7
15	Veneto	2851	4905000	581.2
16	Extremadura	590	1067710	552.6
17	Marche	839	1532000	547.7
18	Cantabria	294	581078	506.0
19	Galicia	1343	2699499	497.5
20	Toscana	1388	3737000	371.4
21	Abruzzo	457	1315000	347.5
22	C. Valenciana	1726	5003769	344.9
23	Friuli Venezia Giulia	383	1216000	315.0
24	Asturias	312	1022800	305.0
25	Baleares	230	1149460	200.1
26	Andalucía	1606	8414240	190.9
27	Melilla	15	86487	173.4
28	Puglia	672	4048000	166.0
29	Lazio	965	5897000	163.6
30	Ceuta	12	84777	141.5
31	Canarias	295	2153389	137.0
32	Murcia	204	1493898	136.6
33	Sardegna	206	1648000	125.0
34	Campania	644	5827000	110.5
35	Molise	33	308493	107.0
36	Umbria	80	884640	90.4
37	Sicilia	422	5027000	83.9
38	Calabria	128	1957000	65.4
39	Basilicata	37	567118	65.2

x = esp_df %>% left_join(esp_pop) %>%
  bind_rows(ita %>% left_join(ita_pop)) %>%
  filter(date == '2020-04-09') %>%
  filter(ccaa %in% c('Madrid',
                     'Cataluña', 'Lombardia')) %>%
  dplyr::select(ccaa, deaths, cases, pop) %>%
  mutate(deathsp = deaths / pop * 100000,
         casesp = cases / pop * 100000)


covid19::plot_day_zero(countries = c('Italy', 'Spain', 'China', 'South Korea', 'Sinagpore'),
                       districts = c('Madrid', #'Hubei',
                     'Cataluña', 'Lombardia'),
                     by_district = T,
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') 

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                     roll = 7,
                     deaths = T,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Morts diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Morts diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                       by_district = T,
                       districts = c('Madrid', 'Emilia-Romagna',
                     'Cataluña', 'Lombardia'),
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

Personas susceptibles

left <- esp_df %>%
  group_by(date) %>%
  summarise(cases = sum(cases))
left$pop <- esp_pop %>% summarise(pop = sum(pop)) %>% .$pop
left$susceptible <- left$pop - (left$cases * 10)
left$p <- left$susceptible / left$pop * 100

ggplot(data = left,
       aes(x = date,
           y = p)) +
  geom_line() +
  ylim(0, 100)

Asia

covid19::plot_day_zero(countries = c('Japan', 'South Korea', 'Singapore', 'Hong Kong'),
                     roll = 7,
                     deaths = F,
                     # pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
    labs(x = 'Data',
       y = 'Casos diaris (mitjana mòbil de 3 dies)',
       title = 'Casos diaris',
       subtitle = 'Mitjana mòbil de 3 dies') +
  facet_wrap(~geo, scales = 'free_y') +
  theme(legend.position = 'none')

Day of week analysis

pd <- esp_df %>%
  arrange(date) %>%
  group_by(date) %>%
  summarise(deaths_non_cum = sum(deaths_non_cum),
            cases_non_cum = sum(cases_non_cum)) %>%
  ungroup %>%  
  mutate(dow = weekdays(date)) %>%
  mutate(week = isoweek(date)) %>%
  group_by(week) %>%
  mutate(start_date = min(date)) %>%
  ungroup %>%
  filter(date >= '2020-03-09')
pd$dow <- factor(pd$dow,
                 levels = c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',
                            'Saturday', 'Sunday'),
                 labels = c('Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes',
                            'Sábado', 'Domingo'))
n_cols <- length(unique(pd$start_date))
cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))(n_cols)
pd$start_date <- factor(pd$start_date)
ggplot(data = pd,
       aes(x = dow,
           y = cases_non_cum,
           group = week,
           color = start_date)) +
  geom_line(size = 4) +
  geom_point(size = 4) +
  scale_color_manual(name = 'Primer día\nde la semana',
                     values = cols) +
  theme_simple() +
  labs(x = 'Día de la semana',
       y = 'Muertes')

Basque country vs rest of Spain

pd <- esp_df %>%
  mutate(geo = ifelse(ccaa == 'País Vasco', 'Basque country', 'Rest of Spain')) %>%
  group_by(geo, date) %>%
  summarise(deaths = sum(deaths)) %>%
  ungroup
pp <- esp_pop %>%
    mutate(geo = ifelse(ccaa == 'País Vasco', 'Basque country', 'Rest of Spain')) %>%
  group_by(geo) %>%
  summarise(pop = sum(pop))
pd <- left_join(pd, pp) %>% mutate(pk = deaths / pop * 100000)

ggplot(data = pd %>% filter(pk > 0.1),
       aes(x = date,
           y = pk,
           color = geo)) +
  geom_line() +
  labs(x = 'Date',
       y = 'Cumulative deaths per 100,000') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme_simple() 

Country trajectories, population adjusted

countries <- c(
  'Spain',
  'US',
  'France',
  # 'Portugal',
  'Italy',
  'China'
)
districts <- c('Lombardia', 'Cataluña', 
               'New York', 
               # 'Hubei',
               'CyL', 
               'CLM', 
               # 'Washington',
               'La Rioja',
               'Madrid')

plot_day_zero(countries = countries,
              districts = districts,
              ylog = F,
              day0 = 1,
              cumulative = F,
              time_before = 0,
              roll = 3,
              deaths = T,
              pop = T,
              by_district = T,
              point_alpha = 0,
              line_size = 3,
              color_var = 'geo')

Italy and Spain

dir.create('/tmp/totesmou')
plot_day_zero(countries = c('Spain', 'Italy', max_date = Sys.Date()-1),
              point_size = 2, calendar = T)

ggsave('/tmp/totesmou/1_italy_vs_spain.png',
       height = 5.6,
       width = 9.6)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F)

ggsave('/tmp/totesmou/2_italy_vs_spain_temps_ajustat.png',
       height = 5.6,
       width = 9.6)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = T, deaths = T, day0 = 10)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 10)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 10, pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 1, pop = T, roll = 3, roll_fun = 'mean')

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              day0 = 10,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              day0 = 1,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              roll = 3,
              roll_fun = 'mean',
              day0 = 1,
              ylog = F,
              pop = T, calendar = T)

plot_day_zero(countries = c('Spain', 'Italy', 'US'),
              districts = c('Cataluña', 'Madrid', 
                            'New York', 
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              roll = 3,
              roll_fun = 'mean',
              day0 = 1,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy', 'US'),
              districts = c('Cataluña', 'Madrid', 
                            'New York', 
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              # roll = 7,
              roll_fun = 'mean',
              day0 = 1,
              pop = F)

plot_day_zero(color_var = 'iso', by_district = T,
              deaths = T,
              day0 = 1,
              alpha = 0.6,
              point_alpha = 0,
              calendar = T,
              countries = c('Spain', 'Italy'),
              pop = T)

place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Altres CCAA')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Altres regions italianes')
  # )
}
pd <- esp_df %>% mutate(country = 'Espanya') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Itàlia')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Altres CCAA',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Altres regions italianes'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Itàlia', 'Espanya')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9))
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=50])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  facet_wrap(~country) +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Data',
       y = 'Percentatge de morts',
       title = 'Percentatge de morts: regió més afectada vs resta del pais',
       subtitle = 'A partir del primer día a cada país amb 50 morts acumulades') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5)) +
  geom_text(data = label_data,
            aes(x = x-2,
                y = y,
                label = label,
                color = ccaa),
            size = 7,
            show.legend = FALSE)

# Same cahrt as previous, but one shared axis
place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Rest of Spain')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Rest of Italy')
  # )
}
pd <- esp_df %>% mutate(country = 'Spain') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italy')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Rest of Spain',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Rest of Italy'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Italy', 'Spain')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  # mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9)) %>%
  ungroup
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=30])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  # facet_wrap(~country, scales = 'free_x') +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Date',
       y = 'Percentage of deaths',
       title = 'Percentage of country\'s cumulative COVID-19 deaths by geography',
       subtitle = 'Starting at first day with 50 or more cumulative deaths') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5))# +

  # geom_text(data = label_data,
  #           aes(x = x-2,
  #               y = y,
  #               label = label,
  #               color = ccaa),
  #           size = 7,
  #           show.legend = FALSE)

Same chart as above but absolute numbers

# Same cahrt as previous, but one shared axis
place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Rest of Spain')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Rest of Italy')
  # )
}
pd <- esp_df %>% mutate(country = 'Spain') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italy')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = rev(c('Madrid',
                             # 'Cataluña',
                             'Rest of Spain',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Rest of Italy')))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Italy', 'Spain')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  # mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9)) %>%
  ungroup

# Get moving
ma <- function(x, n = 2){
    
    if(length(x) >= n){
      stats::filter(x, rep(1 / n, n), sides = 1)
    } else {
      new_n <- length(x)
      stats::filter(x, rep(1 / new_n, new_n), sides = 1)
    }
}


ggplot(data = pd %>% group_by(country) %>% 
         mutate(start_day = dplyr::first(date[deaths >=1])) %>% 
         filter(date >= start_day) %>% 
         mutate(days_since = as.numeric(date - start_day)) %>%
         ungroup %>% arrange(date) %>%
         group_by(country, ccaa) %>%
         mutate(rolling_average = ma(deaths_non_cum, n = 3)) %>%
         ungroup,
       aes(x = date,
           y = rolling_average,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  # scale_y_log10(limits = c(1.5, 1000)) +
  # scale_y_log10() +
  facet_wrap(~country) +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  # geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Date',
       y = 'Deaths (log-scale)',
       title = 'Daily COVID-19 deaths by geography',
       subtitle = '3-day rolling average') +
  theme(legend.position = 'top',
        plot.title = element_text(size = 30),
        plot.subtitle = element_text(size = 24),
        strip.text = element_text(size = 30, hjust = 0.5),
        legend.text = element_text(size = 20))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5))

ggsave('~/Desktop/madlom.png')

roll_curve <- function(data,
                       n = 4,
                       deaths = FALSE,
                       scales = 'fixed',
                       nrow = NULL,
                       ncol = NULL,
                       pop = FALSE){

  # Create the n day rolling avg
  ma <- function(x, n = 5){
    
    if(length(x) >= n){
      stats::filter(x, rep(1 / n, n), sides = 1)
    } else {
      new_n <- length(x)
      stats::filter(x, rep(1 / new_n, new_n), sides = 1)
    }
    
    
  }
  
  pd <- data
  if(deaths){
    pd$var <- pd$deaths_non_cum
  } else {
    pd$var <- pd$cases_non_cum
  }
  
  if('ccaa' %in% names(pd)){
    pd$geo <- pd$ccaa
  } else {
    pd$geo <- pd$iso
  }
  
  if(pop){
    # try to get population
    if(any(pd$geo %in% unique(esp_df$ccaa))){
      right <- esp_pop
      right_var <- 'ccaa'
    } else {
      right <- world_pop
      right_var <- 'iso'
    }
    pd <- pd %>% left_join(right %>% dplyr::select(all_of(right_var), pop),
                           by = c('geo' = right_var)) %>%
      mutate(var = var / pop * 100000)
  }
  
  pd <- pd %>%
    arrange(date) %>%
    group_by(geo) %>%
    mutate(with_lag = ma(var, n = n))
  
  
  ggplot() +
    geom_bar(data = pd,
         aes(x = date,
             y = var),
             stat = 'identity',
         fill = 'grey',
         alpha = 0.8) +
    geom_area(data = pd,
              aes(x = date,
                  y = with_lag),
              color = 'red',
              fill = 'red',
              alpha = 0.6) +
    facet_wrap(~geo, scales = scales, nrow = nrow, ncol = ncol) +
    theme_simple() +
    labs(x = '',
         y = ifelse(deaths, 'Deaths', 'Cases'),
         title = paste0('Daily (non-cumulative) ', ifelse(deaths, 'deaths', 'cases'),
                        ifelse(pop, ' (per 100,000)', '')),
         subtitle = paste0('Data as of ', max(data$date),
                           '.\nRed line = ', n, ' day rolling average. Grey bar = day-specific value.')) +
    theme(axis.text.x = element_text(size = 7,
                                     angle = 90,
                                     hjust = 0.5, vjust = 1)) #+
    # scale_x_date(name ='',
    #              breaks = sort(unique(pd$date)),
    #              labels = format(sort(unique(pd$date)), '%b %d'))
    # scale_y_log10()
}
this_ccaa <- 'Cataluña'
plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, scales = 'fixed')  + theme(strip.text = element_text(size = 30))

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, deaths = T, scales = 'fixed') + theme(strip.text = element_text(size = 30))

african_countries <-  world_pop$country[world_pop$sub_region %in% c('Sub-Saharan Africa')]

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 13)
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 6)
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date(),
                    ylog = F)
pd

latam_countries <-  world_pop$country[world_pop$sub_region %in% c('Latin America and the Caribbean')]
latam_countries <- latam_countries[!latam_countries %in% c('Guyana')]

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 13)
pd

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 6)
pd

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date())
pd

Latin America and Africa vs Europe

isos <- sort(unique(world_pop$sub_region))
keep_countries <- world_pop$country[world_pop$sub_region %in% c('Latin America and the Caribbean', 'Sub-Saharan Africa') |
                                      world_pop$region %in% 'Europe']
keep_countries <- keep_countries[!keep_countries %in% c('Guyana')]
pd <- df_country %>% ungroup %>%
  filter(country %in% keep_countries) %>%
  dplyr::select(-country) %>%
  left_join(world_pop) %>%
  group_by(iso) %>%
  mutate(day0 = min(date[cases >= 10])) %>%
  ungroup %>%
  mutate(days_since = date - day0) %>%
  filter(days_since >= 0)


cols <- c( 'black')
g <- ggplot(data = pd,
       aes(x = days_since,
           y = cases,
           group = country,
           color = region)) +
  geom_line(data = pd %>% filter(region == 'Europe'),
            alpha = 0.6) +
  scale_y_log10() +
  scale_color_manual(name = '', values = cols) +
  theme_simple() +
  labs(x = 'Days since first day at 10 cases') +
  theme(legend.position = 'top')
g

cols <- c('darkred', 'black')

g + 
    geom_line(data = pd %>% filter(region == 'Africa'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) +
  scale_y_continuous()

cols <- c( 'darkorange', 'black')
g + 
    geom_line(data = pd %>% filter(sub_region == 'Latin America and the Caribbean'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) 

cols <- c('darkred', 'darkorange', 'black')
g + 
    geom_line(data = pd %>% filter(sub_region != 'Europe'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) 

# Assets
pyramid_dir <- '../../data-raw/pyramids/'
pyramid_files <- dir(pyramid_dir)
out_list <- list()
for(i in 1:length(pyramid_files)){
  out_list[[i]] <- read_csv(paste0(pyramid_dir, pyramid_files[i])) %>%
    mutate(region = gsub('.csv', '', pyramid_files[i]))
}
pyramid <- bind_rows(out_list)
make_pyramid <- function(the_region = 'AFRICA-2019'){
  sub_data <- pyramid %>% filter(region == the_region)
  sub_data$Age <- factor(sub_data$Age, levels = sub_data$Age)
  sub_data <- tidyr::gather(sub_data, key, value, M:F)
  ggplot(data = sub_data,
         aes(x = Age,
             y = value,
             fill = key)) +
    geom_bar(stat = 'identity',
             position = position_dodge()) +
    scale_fill_manual(name = '', values = c('darkgrey', 'lightblue')) +
    theme_simple() +
    labs(x = 'Age group',
         y = 'Population') +
    theme(legend.position = 'top')
}
make_pyramid_overlay <- function(){
  sub_data <- pyramid %>% filter(region %in% c('EUROPE-2019',
                                               'AFRICA-2019',
                                               'LATIN AMERICA AND THE CARIBBEAN-2019')) %>%
    mutate(region = gsub('-2019', '', region))
   sub_data$Age <- factor(sub_data$Age, levels = unique(sub_data$Age))
  sub_data <- tidyr::gather(sub_data, key, value, M:F) %>%
    group_by(Age, region) %>%
    summarise(value = sum(value)) %>%
    ungroup %>%
    group_by(region) %>%
    mutate(p = value / sum(value) * 100) %>%
    ungroup
  ggplot(data = sub_data,
         aes(x = Age,
             y = p,
             color = region,
             group = region,
             fill = region)) +
    geom_area(position = position_dodge(),
              alpha = 0.6) +
    scale_fill_manual(name = '',
                      values = c('darkred', 'darkorange', 'black')) +
    scale_color_manual(name = '',
                      values = c('darkred', 'darkorange', 'black')) +
    theme_simple() +
    theme(legend.position = 'top') +
    labs(x = 'Age group', y = 'Percentage')
}

make_pyramid(the_region = 'Spain-2019') + labs(title = 'Spain')
make_pyramid(the_region = 'Italy-2019') + labs(title = 'Italy')

make_pyramid(the_region = 'EUROPE-2019') + labs(title = 'Europe')
make_pyramid(the_region = 'Kenya-2019') + labs(title = 'Kenya')


make_pyramid(the_region = 'Guatemala-2019') + labs(title = 'Guatemala')

make_pyramid(the_region = 'AFRICA-2019') + labs(title = 'Africa')

make_pyramid(the_region = 'LATIN AMERICA AND THE CARIBBEAN-2019') + labs(title = 'Latin America and the Caribbean')

make_pyramid_overlay() + labs(title = 'Population distribution by region')

Pics

plot_day_zero(countries = c('China', 'Italy', 'Spain'),
              districts = c('Hubei', 'Lombardia', 'Cataluña', 'Madrid'),
              by_district = T,
              point_alpha = 0,
              day0 = 5,
              pop = F,
              deaths = T,
              ylog = T,
              calendar = F,
              roll = 5)

Map of portugal, france, spain

# cat_transform <- function(x){ifelse(x == 'Catalunya', 'Cataluña', x)}
cat_transform <- function(x){return(x)}
pd <- por_df %>% mutate(country = 'Portugal') %>%
  bind_rows(esp_df %>% mutate(country = 'Spain')) %>%
  bind_rows(fra_df %>% mutate(country = 'France')) %>%
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(
    df %>% filter(country == 'Andorra') %>%
      mutate(ccaa = 'Andorra')
  )
keep_date = pd %>% group_by(country) %>% summarise(max_date= max(date)) %>% summarise(x = min(max_date)) %>% .$x
pd <- pd %>%
  mutate(ccaa = cat_transform(ccaa)) %>%
  group_by(ccaa) %>%
  filter(date == keep_date) %>%
  # filter(date == '2020-03-27') %>%
  ungroup %>%
  dplyr::select(date, ccaa, deaths, deaths_non_cum,
                cases, cases_non_cum) %>%
  left_join(regions_pop %>%
              bind_rows(
                world_pop %>% filter(country == 'Andorra') %>% dplyr::mutate(ccaa = country) %>%
                  dplyr::select(-region, -sub_region)
              )) %>%
  mutate(cases_per_million = cases / pop * 1000000,
         deaths_per_million = deaths / pop * 1000000) %>%
  mutate(cases_per_million_non_cum = cases_non_cum / pop * 1000000,
         deaths_per_million_non_cum = deaths_non_cum / pop * 1000000)

map_esp1 <- map_esp
map_esp1@data <- map_esp1@data %>% dplyr::select(ccaa)
map_fra1 <- map_fra
map_fra1@data <- map_fra1@data %>% dplyr::select(ccaa = NAME_1)
map_por1 <- map_por
map_por1@data <- map_por1@data %>% dplyr::select(ccaa = CCDR)
map_ita1 <- map_ita 
map_ita1@data <- map_ita1@data %>% dplyr::select(ccaa)
map_and1 <- map_and
map_and1@data <- map_and1@data %>% dplyr::select(ccaa = NAME_0)


map <- 
  rbind(map_esp1,
        map_por1,
        map_fra1,
        map_ita1,
        map_and1)

# Remove areas not of interest
centroids <- coordinates(map)
centroids <- data.frame(centroids)
names(centroids) <- c('x', 'y')
centroids$ccaa <- map@data$ccaa
centroids <- left_join(centroids, pd)
# map <- map_sp <- map[centroids$y >35 & centroids$x > -10 &
#              centroids$x < 8 & (centroids$y < 43  | map@data$ccaa %in% c('Occitanie', 'Nouvelle-Aquitaine') |
#                                   map@data$ccaa %in% esp_df$ccaa),]
# map_sp <- map <-  map[centroids$x > -10 & centroids$y <47,]
map_sp <- map <-  map[centroids$x > -10 & centroids$y <77,]

# map_sp <- map <-  map[centroids$x > -10,]

# fortify
map <- fortify(map, region = 'ccaa')

# join data
map$ccaa <- map$id
map <- left_join(map, pd)
var <- 'deaths_per_million'
map$var <- as.numeric(unlist(map[,var]))
centroids <- centroids[,c('ccaa', 'x', 'y', var)]
centroids <- centroids %>%
  filter(ccaa %in% map_sp@data$ccaa)

# cols <- rev(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))
# cols <- c('#A16928','#bd925a','#d6bd8d','#edeac2','#b5c8b8','#79a7ac','#2887a1')
# cols <- c('#009392','#39b185','#9ccb86','#e9e29c','#eeb479','#e88471','#cf597e')
# cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
cols <- rev(colorRampPalette(c('darkred', 'red', 'darkorange', 'orange', 'yellow', 'lightblue'))(10))
g <- ggplot(data = map,
         aes(x = long,
             y = lat,
             group = group)) +
    geom_polygon(aes(fill = var),
                 lwd = 0.3,
                 # color = 'darkgrey',
                 color = NA,
                 size = 0.6) +
      scale_fill_gradientn(name = '',
                           colours = cols) +
  # scale_fill_() +
  ggthemes::theme_map() +
  theme(legend.position = 'bottom',
        plot.title = element_text(size = 16)) +
  guides(fill = guide_colorbar(direction= 'horizontal',
                               barwidth = 50,
                               barheight = 1,
                               label.position = 'bottom')) +
  labs(title = 'Cumulative COVID-19 deaths per million population, western Mediterranean',
       subtitle = paste0('Data as of ', format(max(pd$date), '%B %d, %Y'))) +
  geom_text(data = centroids,
            aes(x = x,
                y = y,
                group = NA,
                label = paste0(ccaa, '\n',
                               round(deaths_per_million, digits = 2))),
            alpha = 0.8,
            size = 3)
g

ggsave('/tmp/map_with_borders.png',
       height = 8, width = 13)

Animation, Portugal, France, Spain, Italy

dir.create('/tmp/animation_map/')
pd <- por_df %>% mutate(country = 'Portugal') %>%
  bind_rows(esp_df %>% mutate(country = 'Spain')) %>%
  bind_rows(fra_df %>% mutate(country = 'France')) %>%
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(
    df %>% filter(country == 'Andorra') %>%
      mutate(ccaa = 'Andorra')
  )
pd %>% group_by(country) %>% summarise(max_date= max(date))

# A tibble: 5 x 2
  country  max_date  
  <chr>    <date>    
1 Andorra  2020-04-12
2 France   2020-04-11
3 Italy    2020-04-12
4 Portugal 2020-04-12
5 Spain    2020-04-12

unique_dates <- sort(unique(pd$date))
unique_dates <- unique_dates[unique_dates >= '2020-03-01']
popper <- regions_pop %>%
                bind_rows(
                  world_pop %>% filter(country == 'Andorra') %>% dplyr::mutate(ccaa = country) %>%
                    dplyr::select(-region, -sub_region)
                )


map_esp1 <- map_esp
map_esp1@data <- map_esp1@data %>% dplyr::select(ccaa)
map_fra1 <- map_fra
map_fra1@data <- map_fra1@data %>% dplyr::select(ccaa = NAME_1)
map_por1 <- map_por
map_por1@data <- map_por1@data %>% dplyr::select(ccaa = CCDR)
map_ita1 <- map_ita 
map_ita1@data <- map_ita1@data %>% dplyr::select(ccaa)
map_and1 <- map_and
map_and1@data <- map_and1@data %>% dplyr::select(ccaa = NAME_0)


map <- 
  rbind(map_esp1,
        map_por1,
        map_fra1,
        map_ita1,
        map_and1)

# Remove areas not of interest
centroids <- coordinates(map)
centroids <- data.frame(centroids)
names(centroids) <- c('x', 'y')
centroids$ccaa <- map@data$ccaa
# map <- map_sp <- map[centroids$y >35 & centroids$x > -10 &
#              # centroids$x < 8 &
#                (centroids$y < 43  | map@data$ccaa %in% c('Occitanie', 'Nouvelle-Aquitaine') |
#                                   map@data$ccaa %in% esp_df$ccaa),]
# map_sp <- map <-  map[centroids$x > -10 & centroids$y <47,]
map_sp <- map <-  map[centroids$x > -10 & centroids$y <477,]


# fortify
map <- fortify(map, region = 'ccaa')



for(i in 1:length(unique_dates)){
  this_date <- unique_dates[i]
    today_map <- map
    today_centroids <- centroids
    today_pd <- pd

  today_pd <- today_pd %>%
      mutate(ccaa = cat_transform(ccaa)) %>%
    group_by(ccaa) %>%
    # filter(date == max(date)) %>%
    filter(date == this_date) %>%
    ungroup %>%
    dplyr::select(date, ccaa, deaths, deaths_non_cum,
                  cases, cases_non_cum) %>%
    left_join(popper) %>%
    mutate(cases_per_million = cases / pop * 1000000,
           deaths_per_million = deaths / pop * 1000000) %>%
    mutate(cases_per_million_non_cum = cases_non_cum / pop * 1000000,
           deaths_per_million_non_cum = deaths_non_cum / pop * 1000000)
  
  today_centroids <- left_join(today_centroids, today_pd)

  
  # join data
  today_map$ccaa <- today_map$id
  today_map <- left_join(today_map, today_pd)
  var <- 'deaths_per_million'
  today_map$var <- as.numeric(unlist(today_map[,var]))
  today_map$var <- ifelse(is.na(today_map$var), 0, today_map$var)
  today_centroids <- today_centroids[,c('ccaa', 'x', 'y', var)]
  today_centroids <- today_centroids %>%
    filter(ccaa %in% today_map$ccaa)
  today_centroids$var <- today_centroids[,var]
  today_centroids$var <- ifelse(is.na(today_centroids$var), 0, today_centroids$var)

  
  # cols <- rev(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))
  # cols <- c('#A16928','#bd925a','#d6bd8d','#edeac2','#b5c8b8','#79a7ac','#2887a1')
  # cols <- c('#009392','#39b185','#9ccb86','#e9e29c','#eeb479','#e88471','#cf597e')
  # cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
  cols <- rev(colorRampPalette(c('darkred', 'red', 'darkorange', 'orange', 'yellow', 'white'))(17))
  g <- ggplot(data = today_map,
           aes(x = long,
               y = lat,
               group = group)) +
      geom_polygon(aes(fill = var),
                   lwd = 0.3,
                   # color = 'darkgrey',
                   color = NA,
                   size = 0.6) +
        scale_fill_gradientn(name = '',
                             colours = cols,
                             breaks = seq(0, 1100, 50),
                             limits = c(0, 1100)) +
    # scale_fill_() +
    ggthemes::theme_map() +
    theme(legend.position = 'right',
          plot.title = element_text(size = 24)) +
    guides(fill = guide_colorbar(direction= 'vertical',
                                 barwidth = 1,
                                 barheight = 30,
                                 label.position = 'left')) +
    labs(subtitle = 'Cumulative COVID-19 deaths per million population',
         title = paste0(format(this_date, '%B %d, %Y'))) +
    geom_text(data = today_centroids,
              aes(x = x,
                  y = y,
                  group = NA,
                  label = paste0(ccaa, '\n',
                                 round(var, digits = 2))),
              alpha = 0.8,
              size = 1.5)
  
  ggsave(paste0('/tmp/animation_map/', this_date, '.png'),
         plot = g,
         height = 6, width = 9)
}

# Command line
cd /tmp/animation_map
mogrify -resize 40% *.png
convert -delay 20 -loop 0 *.png result.gif

Deaths overall over time Spain

df_country %>% filter(country == 'Spain') %>% arrange(date) %>% tail

# A tibble: 6 x 10
# Groups:   country [1]
  country date        cases deaths   uci hospitalizations cases_non_cum
  <chr>   <date>      <dbl>  <dbl> <dbl>            <int>         <dbl>
1 Spain   2020-04-07 146690  14555  7130                0          6179
2 Spain   2020-04-08 152446  15238  7371                0          5756
3 Spain   2020-04-09 157022  15843  7482                0          4576
4 Spain   2020-04-10 161852  16353  7535                0          4830
5 Spain   2020-04-11 166019  16972  7554                0          4167
6 Spain   2020-04-12 169496  17489  7777                0          3477
# … with 3 more variables: deaths_non_cum <dbl>, uci_non_cum <dbl>, iso <chr>

Deaths yesterday

pd <- df_country
pd$value <- pd$deaths_non_cum
the_date <- Sys.Date() - 1
pd <- pd %>%
  filter(date == the_date) %>%
  dplyr::select(country, iso, cases, cases_non_cum,
                deaths, value) %>%
  dplyr::arrange(desc(value)) %>%
  left_join(world_pop %>% dplyr::select(-country)) %>%
  mutate(value_per_million = value / pop * 1000000) #%>% 
  # arrange(desc(value_per_million))
pd <- pd[1:10,]
pd$country <- factor(pd$country, levels = pd$country)
ggplot(data = pd,
       aes(x = country,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  geom_text(aes(label = value),
            nudge_y = -20,
            size = 4,
            color = 'white') +
  labs(title = paste0('Confirmed COVID-19 deaths on ', the_date),
       x = '', y = '')

pd

# A tibble: 10 x 10
# Groups:   country [10]
   country iso    cases cases_non_cum deaths value    pop region sub_region
   <fct>   <chr>  <dbl>         <dbl>  <dbl> <dbl>  <dbl> <chr>  <chr>     
 1 US      USA   555313         28917  22020  1557 3.27e8 Ameri… Northern …
 2 United… GBR    85206          5332  10629   737 6.65e7 Europe Northern …
 3 France  FRA   133670          2943  14412   561 6.70e7 Europe Western E…
 4 Spain   ESP   169496          3477  17489   517 4.67e7 Europe Southern …
 5 Italy   ITA   156363          4092  19899   431 6.04e7 Europe Southern …
 6 Germany DEU   127854          2946   3022   286 8.29e7 Europe Western E…
 7 Belgium BEL    29647          1629   3600   254 1.14e7 Europe Western E…
 8 Iran    IRN    71686          1657   4474   117 8.18e7 Asia   Southern …
 9 Brazil  BRA    22192          1465   1223    99 2.09e8 Ameri… Latin Ame…
10 Turkey  TUR    56956          4789   1198    97 8.23e7 Asia   Western A…
# … with 1 more variable: value_per_million <dbl>

Deaths per million yesterday per CCAA

pd <- esp_df
pd$value <- pd$deaths_non_cum
the_date <- max(pd$date)
pd <- pd %>%
  filter(date == max(date)) %>%
  dplyr::select(ccaa, cases, cases_non_cum,
                deaths, value) %>%
  dplyr::arrange(desc(value)) %>%
  left_join(esp_pop)%>%
  mutate(value_per_million = value / pop * 1000000) #%>% 
  # arrange(desc(value_per_million))
pd <- pd[1:10,]
pd$ccaa <- factor(pd$ccaa, levels = pd$ccaa)
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  geom_text(aes(label = value),
            nudge_y = -20,
            size = 4,
            color = 'white')

pd

# A tibble: 10 x 7
   ccaa          cases cases_non_cum deaths value     pop value_per_million
   <fct>         <dbl>         <dbl>  <dbl> <dbl>   <dbl>             <dbl>
 1 Madrid        47146           559   6423   145 6663394             21.8 
 2 Cataluña      34726           699   3538    96 7675217             12.5 
 3 CLM           14054           356   1626    83 2032863             40.8 
 4 CyL           12628           510   1263    42 2399548             17.5 
 5 C. Valenciana  9060           219    876    38 5003769              7.59
 6 País Vasco    11018           246    831    27 2207776             12.2 
 7 Andalucía     10187           181    815    16 8414240              1.90
 8 Aragón         4187           117    464    14 1319291             10.6 
 9 Galicia        7494           158    284    10 2699499              3.70
10 Asturias       1958            66    149     9 1022800              8.80

Deaths yesterday animation

dir.create('/tmp/animation_deaths')
dates <- seq(as.Date('2020-03-17'), (Sys.Date()-1), by = 1)
for(i in 1:length(dates)){
  this_date <- dates[i]
  pd <- df_country
  pd$value <- pd$deaths_non_cum
  pd <- pd %>%
    filter(date == max(this_date)) %>%
    dplyr::select(country, cases, cases_non_cum,
                  deaths, value) %>%
    dplyr::arrange(desc(value))
  pd <- pd[1:10,]
  pd <- pd %>% filter(value > 0)
  pd$country <- gsub(' ', '\n', pd$country)
  pd$country <- factor(pd$country, levels = pd$country)
  pd$color_var <- pd$country == 'Spain'
  if('Spain' %in% pd$country){
    cols <- rev(c('darkred', 'black'))
  } else {
    cols <- 'black'
  }
  g <- ggplot(data = pd,
         aes(x = country,
             y = value)) +
    geom_bar(stat = 'identity',
             aes(fill = color_var),
             alpha = 0.8,
             show.legend = FALSE) +
    theme_simple() +
    geom_text(aes(label = value),
              nudge_y = max(pd$value) * .05,
              size = 5,
              color = 'black') +
    labs(title = 'Daily (non-cumulative) COVID-19 deaths',
         subtitle = format(this_date, '%B %d')) +
    labs(x = 'Country',
         y = 'Deaths') +
    theme(axis.text = element_text(size = 15),
          plot.subtitle = element_text(size = 20)) +
    scale_fill_manual(name ='',
                      values = cols) +
    ylim(0, 900)
  ggsave(filename = paste0('/tmp/animation_deaths/', this_date, '.png'),
         g)
}
# Command line

cd /tmp/animation_deaths
mogrify -resize 50% *.png
convert -delay 50 -loop 0 *.png result.gif

Heatmap

pd <- by_country <-  esp_df %>% mutate(country = 'Spain') %>% 
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(por_df %>% mutate(country = 'Portugal')) %>%
  bind_rows(fra_df %>% mutate(country = 'France'))
pd$value <- pd$deaths_non_cum
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% ungroup %>% summarise(d = min(d)) %>% .$d
# pd$value <- ifelse(is.na(pd$value), 0, pd$value)
left <- expand.grid(date = seq(min(pd$date),
                               max(pd$date),
                               by = 1),
                    ccaa = sort(unique(pd$ccaa)))
right <- pd %>% dplyr::select(date, ccaa, value)
pd <- left_join(left, right) %>% mutate(value = ifelse(is.na(value), NA, value))
pd <- left_join(pd, by_country %>% dplyr::distinct(country, ccaa)) %>%
  filter(date <= max_date) %>%
  filter(value > 0)
the_limits <- c(1, 600)
the_breaks <- c(1, seq(100, 600, length = 6)) #seq(0, 600, length = 7)
pd$ccaa <- factor(pd$ccaa, levels = rev(unique(sort(pd$ccaa))))
ggplot(data = pd,
       aes(x = date,
           y = ccaa,
           color = value,
           size = value)) +
  # geom_tile(color = 'white') +
  geom_point(alpha = 0.8) +
  # geom_tile() +
  scale_color_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
                        name = '',
                        limits = the_limits,
                        breaks = the_breaks) +
    # scale_fill_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
    #                     name = '',
    #                     limits = the_limits,
    #                     breaks = the_breaks) +
  scale_size_area(name = '', limits = the_limits, breaks = the_breaks, max_size = 10) +
  
  theme_simple() +
  facet_wrap(~country, scales = 'free_y') +
  theme(strip.text = element_text(size = 20),
        axis.title = element_blank(),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(size = 12)) +
  guides(color = guide_legend(), size = guide_legend()) +
  labs(title = 'Daily (non-cumulative) COVID-19 deaths by sub-state regions',
       caption = paste0('Data as of ', max_date))

ggsave('/tmp/1.png',
       width = 10,
       height = 8)

Heatmap per population

pd <- by_country <-  esp_df %>% mutate(country = 'Spain') %>%  bind_rows(ita %>% mutate(country = 'Italy'))
poppy <- bind_rows(ita_pop, esp_pop)
pd <- pd %>% left_join(poppy)
pd$value <- pd$deaths_non_cum / pd$pop * 1000000
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% ungroup %>% summarise(d = min(d)) %>% .$d
# pd$value <- ifelse(is.na(pd$value), 0, pd$value)
left <- expand.grid(date = seq(min(pd$date),
                               max(pd$date),
                               by = 1),
                    ccaa = sort(unique(pd$ccaa)))
right <- pd %>% dplyr::select(date, ccaa, value)
pd <- left_join(left, right) %>% mutate(value = ifelse(is.na(value), NA, value))
pd <- left_join(pd, by_country %>% dplyr::distinct(country, ccaa)) %>%
  filter(date <= max_date) %>%
  filter(value > 0)
the_limits <- c(1, 60)
the_breaks <- c(1, seq(10, 60, length = 6)) #seq(0, 600, length = 7)
pd$ccaa <- factor(pd$ccaa, levels = rev(unique(sort(pd$ccaa))))
ggplot(data = pd,
       aes(x = date,
           y = ccaa,
           color = value,
           size = value)) +
  # geom_tile(color = 'white') +
  geom_point(alpha = 0.8) +
  scale_color_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
                        name = '',
                        limits = the_limits,
                        breaks = the_breaks) +
  scale_size_area(name = '', limits = the_limits, breaks = the_breaks, max_size = 10) +
  theme_simple() +
  facet_wrap(~country, scales = 'free') +
  theme(strip.text = element_text(size = 26),
        axis.title = element_blank(),
        axis.text = element_text(size = 16),
        axis.text.x = element_text(size = 12)) +
  guides(color = guide_legend(), size = guide_legend()) +
  labs(title = 'Daily COVID-19 deaths per 1,000,000 population by sub-state regions',
       caption = paste0('Data as of ', max_date))

ggsave('/tmp/2.png',
       width = 10,
       height = 8)

Madrid vs rest of state

place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Otras CCAA')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Otras regiones italianas')
  # )
}
pd <- esp_df %>% mutate(country = 'España') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italia')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Otras CCAA',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Otras regiones italianas'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)
ggplot(data = pd,
       aes(x = date,
           y = deaths_non_cum_p,
           fill = ccaa,
           group = ccaa)) +
  geom_bar(stat = 'identity',
           position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  # geom_line(size = 0.2,
  #           aes(color = ccaa)) +
  xlim(as.Date('2020-03-09'),
       Sys.Date()-1) +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Muertes diarias por 1.000.000',
       title = 'Muertes por 1.000.000 habitantes') +
  theme(legend.position = 'top') +
  geom_text(aes(label = round(deaths_non_cum_p, digits = 1),
                color = ccaa,
                y = deaths_non_cum_p + 2,
                group = ccaa),
            size = 2.5,
            angle = 90,
            position = position_dodge(width = 0.99))

label_data <- pd %>%
  filter(country  %in% c('Italia', 'España')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -10))
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=50])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  facet_wrap(~country, scales = 'free_x') +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Fecha',
       y = 'Porcentaje de muertes',
       title = 'Porcentaje de muertes del país: región más afectada vs. resto del país',
       subtitle = 'A partir del primer día en cada país con 50 o más muertes') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5)) +
  geom_text(data = label_data,
            aes(x = x - 2,
                y = y,
                label = label,
                color = ccaa),
            size = 6,
            show.legend = FALSE)

Italy regions, Spanish regions, Chinese regions (adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'Hubei (China)',
                          ifelse(country == 'Italy', 'Italia', 'España')))

lombardy_location <- (max(pd_big$days_since_start_deaths_pm[pd_big$ccaa == 'Lombardia']))

# Define label data
label_data <- pd %>% group_by(ccaa) %>% filter(
                                                          (
                                                            (country == 'Hubei (China)' & days_since_start_deaths_pm == 22) |
                                                            (date == max(date) & country == 'España' & deaths_pm > 40 & days_since_start_deaths_pm >= 7) & ccaa != 'CyL' |
                                                              (date == max(date) & country == 'Italia' &
                                                                 ccaa != 'Liguria' & days_since_start_deaths_pm > 15)
                                                          ))
# Get differential label data based on what to be emphasized
bigs <- c('Madrid', 'Lombardia', 'Hubei')
label_data_big <- label_data %>%
  filter(ccaa %in% bigs)
label_data_small <- label_data %>%
  filter(!ccaa %in% bigs)

pd_big <- pd %>%
  filter(ccaa %in% bigs)
pd_small <- pd %>%
  filter(!ccaa %in% bigs)

# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c('darkred', '#FF6633', '#006666')

ggplot(data = pd_big,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small,
            aes(x = days_since_start_deaths_pm,
                y = deaths_pm,
                color = country),
            alpha = 0.7,
            size = 1) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c(cols)) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde "el comienzo del brote"',
       y = 'Muertes por millón de habitantes',
       title = 'Muertes por 1.000.000 habitantes',
       subtitle = paste0('Dia 0 ("comienzo del brote") = primer día a ', x_deaths_pm, ' o más muertes acumuladas por milión de población\nLíneas rojas: CCAA; líneas verde-azules: regiones italianas; línea naranja: Hubei, China'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths_pm + 0.6,
                y = (deaths_pm + 50),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths_pm + 0.6,
                y = deaths_pm  + (log(deaths_pm)/2),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 16),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 25))  +
  xlim(0, lombardy_location + 5)

Italy regions, Spanish regions, Chinese regions (raw numbers, not adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))

# Define label data
label_data <- pd %>% group_by(ccaa) %>% filter(
                                                          (
                                                            (country == 'China' & deaths >10 & days_since_start_deaths == 29) |
                                                            (date == max(date) & country == 'España' & deaths > 90) |
                                                              (date == max(date) & country == 'Italia' &
                                                                 ccaa != 'Liguria' & days_since_start_deaths > 10)
                                                          ))
# Get differential label data based on what to be emphasized
label_data_big <- label_data %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
label_data_small <- label_data %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))

pd_big <- pd %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
pd_small <- pd %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))

# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c( '#FF6633',  'darkred', '#006666')
ggplot(data = pd_big,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
            alpha = 0.7,
            size = 1) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c(cols)) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 1.6,
                y = ifelse(ccaa == 'Hubei', (deaths -500),
                           ifelse(ccaa == 'Lombardia',  (deaths + 700),
                                   (deaths + 300))),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 1.6,
                align = 'left',
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 30))  +
  xlim(0, 50)

ANIMATION: Italy regions, Spanish regions, Chinese regions (raw numbers, not adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))


add_zero <- function(x, n){
  x <- as.character(x)
  adders <- n - nchar(x)
  adders <- ifelse(adders < 0, 0, adders)
  for (i in 1:length(x)){
    if(!is.na(x[i])){
      x[i] <- paste0(
        paste0(rep('0', adders[i]), collapse = ''),
        x[i],
        collapse = '')  
    } 
  }
  return(x)
}
# # Define label data
# label_data <- pd %>% group_by(ccaa) %>% filter(
#                                                           (
#                                                             (country == 'China' & deaths >10 & days_since_start_deaths == 29) |
#                                                             (date == max(date) & country == 'España' & deaths > 90) |
#                                                               (date == max(date) & country == 'Italia' &
#                                                                  ccaa != 'Liguria' & days_since_start_deaths > 10)
#                                                           ))
# # Get differential label data based on what to be emphasized
# label_data_big <- label_data %>%
#   filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
# label_data_small <- label_data %>%
#   filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
# 
pd_big <- pd %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
pd_small <- pd %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))



# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c( '#FF6633',  'darkred', '#006666')

the_dir <- '/tmp/animation/'
dir.create(the_dir)
the_dates <- sort(unique(c(pd_big$date, pd_small$date)))
for(i in 1:length(the_dates)){
  
  the_date <- the_dates[i]
  pd_big_sub <- pd_big %>% filter(date <= the_date)
  pd_big_current <- pd_big_sub %>% filter(date == the_date)
  pd_small_sub <- pd_small %>% filter(date <= the_date)
  pd_small_current <- pd_small_sub %>% filter(date == the_date)

  label_data_big <-
    pd_big_sub %>%
    filter(ccaa %in% c('Lombardia', 'Madrid', 'Hubei')) %>%
    group_by(ccaa) %>%
    filter(date == max(date)) %>%
    ungroup %>%
    mutate(days_since_start_deaths = ifelse(ccaa == 'Hubei' &
                                              days_since_start_deaths >32,
                                            32,
                                            days_since_start_deaths))
  
  label_data_small <-
    pd_small_sub %>%
    filter(ccaa %in% c('Emilia Romagna',
                       'Cataluña',
                       'CLM',
                       'País Vasco',
                       'Veneto',
                       'Piemonte',
                       'Henan',
                       'Heilongjiang')) %>%
    group_by(ccaa) %>%
    filter(date == max(date))

  n_countries <- length(unique(pd_big_sub$country))
  if(n_countries == 3){
    sub_cols  <- cols
  }
  if(n_countries == 2){
    sub_cols <- cols[c(1,3)]
  }
   if(n_countries == 1){
    sub_cols <- cols[1]
  }
  g <- ggplot(data = pd_big_sub,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small_sub,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
            alpha = 0.7,
            size = 1) +
    geom_point(data = pd_big_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
               size = 3) +
    geom_point(data = pd_small_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
               size = 1, alpha = 0.6) +
    scale_y_log10(limits = c(5, 4500)) +
  scale_color_manual(name = '',
                     values = sub_cols) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = format(the_date, '%d %b'),
       subtitle = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 1,
                y = deaths,
                hjust = 0,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 1.6,
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 35),
        plot.subtitle = element_text(size = 24))  +
  xlim(0, 38) 
  message(i)
  ggsave(paste0(the_dir, add_zero(i, 3), '.png'),
         height = 7,
         width = 10.5)
}

# Command line
cd /tmp/animation
mogrify -resize 50% *.png
convert -delay 20 -loop 0 *.png result.gif

ANIMATION: Spain only

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
joined <- a
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))

bigs <- c('Madrid', 'Cataluña', 'CLM', 'CyL', 'País Vasco', 'La Rioja')
pd_big <- pd %>%
  filter(ccaa %in% bigs)
pd_small <- pd %>%
  filter(!ccaa %in% bigs)



# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- colorRampPalette(c('#A16928','#bd925a','#d6bd8d','#edeac2', '#b5c8b8','#79a7ac','#2887a1'))(length(unique(pd$country)))

the_dir <- '/tmp/animation2/'
dir.create(the_dir)
the_dates <- sort(unique(c(pd_big$date, pd_small$date)))
for(i in 1:length(the_dates)){
  
  the_date <- the_dates[i]
  pd_big_sub <- pd_big %>% filter(date <= the_date)
  pd_big_current <- pd_big_sub %>% filter(date == the_date)
  pd_small_sub <- pd_small %>% filter(date <= the_date)
  pd_small_current <- pd_small_sub %>% filter(date == the_date)

  label_data_big <-
    pd_big_sub %>%
    filter(ccaa %in% bigs) %>%
    group_by(ccaa) %>%
    filter(date == max(date)) %>%
    ungroup
  
  label_data_small <-
    pd_small_sub %>%
    group_by(ccaa) %>%
    filter(date == max(date))
# sub_cols <- colorRampPalette(c('#A16928','#bd925a','#d6bd8d','#edeac2', '#b5c8b8','#79a7ac','#2887a1'))(length(unique(pd$ccaa)))
  sub_cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Dark2'))(length(unique(pd$ccaa)))
  # sub_cols <- rainbow((length(unique(pd$ccaa))))
  
  g <- ggplot(data = pd_big_sub,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = ccaa),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small_sub,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
            alpha = 0.7,
            size = 1) +
    geom_point(data = pd_big_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 3) +
    geom_point(data = pd_small_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 1, alpha = 0.6) +
    geom_point(data = pd,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 1, alpha = 0.01) +
    scale_y_log10(limits = c(5, max(pd$deaths)*1.2),
                  breaks = c(10, 50, 100, 500, 1000)) +
  scale_color_manual(name = '',
                     values = sub_cols) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = format(the_date, '%d %b'),
       subtitle = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc')   +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 35),
        plot.subtitle = element_text(size = 24))  +
  xlim(0, 20) +
    theme(legend.position = 'none')
  message(i)
  if(nrow(label_data_big) > 0){
    g <- g +
      geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 0.2,
                y = deaths,
                hjust = 0,
                label = gsub(' ', ' ', ccaa),
                color = ccaa),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 0.2,
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = ccaa),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE)
  }
  
  ggsave(paste0(the_dir, add_zero(i, 3), '.png'),
         height = 7,
         width = 12)
}

# Command line
cd /tmp/animation
mogrify -resize 50% *.png
convert -delay 25 -loop 0 *.png result.gif

Italy regions for Spanish regions

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# join
joined <- bind_rows(a, b)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

ggplot(data = joined %>% filter(days_since_start_deaths_pm >= 0),
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.8,
            size = 2) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkorange', 'purple')) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Days since "start out outbreak"',
       y = 'Deaths per million',
       title = 'Deaths per capita, Italian regions vs. Spanish autonomous communities',
       subtitle = paste0('Day 0 ("start of outbreak") = first day at ', x_deaths_pm, ' or greater cumulative deaths per million'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = joined %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            (country == 'Spain' & deaths_pm > 25) |
                                                              (country == 'Italy' & days_since_start_deaths_pm > 10)
                                                          )),
            aes(x = days_since_start_deaths_pm + 0.6,
                y = deaths_pm,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 6) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 23)

ggsave('/tmp/italy_comparison.png',
       height = 6,
       width = 10)


# Separate for Catalonia
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
         mutate(country = ifelse(ccaa == 'Cataluña',
                                 'Catalonia',
                                 country)) %>%
  mutate(ccaa = ifelse(ccaa == 'Cataluña', 'Catalunya', ccaa))
pdcat <- pd %>% filter(country == 'Catalonia')
label_data <- pd %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            (country == 'Catalonia') |
                                                            (country == 'Spain' & deaths_pm > 25) |
                                                              (country == 'Italy' & days_since_start_deaths_pm > 10)
                                                          ))
ggplot(data = pd,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.3,
            size = 1.5) +
    geom_line(data = pdcat,
              aes(color = country),
            alpha = 0.8,
            size = 2) +
      geom_point(data = pdcat %>% filter(date == max(date)),
              aes(color = country),
            alpha = 0.8,
            size = 4) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkred', 'darkorange', "purple")) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Dies des del "començament del brot"',
       y = 'Morts per milió',
       title = 'Morts per càpita: Catalunya, comunitats autònomes, regions italianes',
       subtitle = paste0('Dia 0 ("començament del brot") = primer dia a ', x_deaths_pm, ' o més morts acumulades per milió de població'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data,
            aes(x = days_since_start_deaths_pm +0.2 ,
                y = deaths_pm +3,
                hjust = 0,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 6,
            alpha = 0.7) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 24)

ggsave('/tmp/cat_italy_comparison.png',
       height = 6,
       width = 10)


# Straightforward Lombardy, Madrid, Cat comparison
specials <- c('Lombardia', 'Madrid')
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
         mutate(country = ifelse(ccaa == 'Cataluña',
                                 'Catalonia',
                                 country)) %>%
  mutate(ccaa = ifelse(ccaa == 'Cataluña', 'Catalunya', ccaa))
pdcat <- pd %>% filter(ccaa %in%  specials)
label_data <- pd %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            # (country == 'Catalonia') |
                                                            (country == 'Spain' & deaths_pm > 20) |
                                                              (country == 'Italy' & days_since_start_deaths_pm >= 10)
                                                          ))
ggplot(data = pd,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.3,
            size = 1.5) +
    geom_line(data = pdcat,
              aes(color = country),
            alpha = 0.8,
            size = 2) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkred', 'darkorange', "purple")) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Dias desde "el comienzo del brote"',
       y = 'Muertes por millón de habitantes',
       title = 'Muertes acumuladas por 1.000.000 habitantes',
       subtitle = paste0('Dia 0 ("comienzo del brote") = primer día a ', x_deaths_pm, ' o más muertes acumuladas por milión de población'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data %>% filter(!ccaa %in% specials),
            aes(x = days_since_start_deaths_pm + 0.4,
                y = deaths_pm +3,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.5) +
    geom_text(data = label_data %>% filter(ccaa %in% specials),
            aes(x = days_since_start_deaths_pm ,
                y = deaths_pm +30,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.8) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 23)

ggsave('/tmp/mad_lom_italy_comparison.png',
       height = 6,
       width = 10)

Loop for regions of the world

isos <- sort(unique(world_pop$sub_region))
isos <- c('Central Asia', 'Eastern Asia', 'Eastern Europe',
          'Latin America and the Caribbean',
          'Northern Africa', 'Northern America',
          'Nothern Europe',
          'South-eastern Asia',
          'Southern Asia', 'Southern Europe',
          'Sub-Saharan Africa', 'Western Asia', 'Western Europe')
dir.create('/tmp/world')
for(i in 1:length(isos)){
  this_iso <- isos[i]
  message(i, ' ', this_iso)
  countries <- world_pop %>% filter(sub_region == this_iso)
  pd <- df %>%
    filter(!country %in% c('Guyan
                           a', 'Bahamas', 'The Bahamas')) %>%
          group_by(country, iso, date) %>%
          summarise(cases = sum(cases, na.rm = TRUE)) %>%
    ungroup %>%
    group_by(country) %>%
         filter(length(which(cases > 0)) > 1) %>%
    ungroup %>%
         filter(iso %in% countries$iso)
  if(nrow(pd) > 0){
    cols <- colorRampPalette(brewer.pal(n = 8, 'Spectral'))(length(unique(pd$country)))
cols <- sample(cols, length(cols))
    # Plot
n_countries <- (length(unique(pd$country)))
ggplot(data = pd,
       aes(x = date,
           # color = country,
           # fill = country,
           y = cases)) +
  theme_simple() +
  # geom_point() +
  # geom_line() +
  geom_area(fill = 'darkred', alpha = 0.3, color = 'darkred') +
  # scale_color_manual(name = '',
  #                    values = cols) +
  # scale_fill_manual(name = '',
  #                   values = cols) +
  theme(legend.position = 'none',
        axis.text = element_text(size = 6),
        strip.text = element_text(size = ifelse(n_countries > 20, 6,
                                                ifelse(n_countries > 10, 10,
                                                       ifelse(n_countries > 5, 11, 12))) ),
        legend.text = element_text(size = 6)) +
  # scale_y_log10() +
  facet_wrap(~country,
             scales = 'free') +
  labs(x = '',
       y = 'Confirmed cases',
       title = paste0('Confirmed cases of COVID-19 in ', this_iso)) 
  ggsave(paste0('/tmp/world/', this_iso, '.png'),
         width = 12, 
         height = 7)
  }



}

Rolling average new events

plot_data <- df_country %>% filter(country %in% c('Spain', 'France', 'Italy', 'Germany', 'Belgium', 'Norway')) %>% mutate(geo = country)
roll_curve(plot_data, pop = T)

dir.create('/tmp/countries')
roll_curve_country <- function(the_country = 'Spain'){
  plot_data <- df_country %>% filter(country %in% the_country) %>% mutate(geo = country)
  g1 <- roll_curve(plot_data, pop = F)
  g2 <- roll_curve(plot_data, pop = T)
  g3 <- roll_curve(plot_data, pop = F, deaths = T)
  g4 <- roll_curve(plot_data, pop = T, deaths = T)
  ggsave(paste0('/tmp/countries/', the_country, '1.png'), g1)
  ggsave(paste0('/tmp/countries/', the_country, '2.png'), g2)
  ggsave(paste0('/tmp/countries/', the_country, '3.png'), g3)
  ggsave(paste0('/tmp/countries/', the_country, '4.png'), g4)
}


countries <- c('Spain', 'France', 'Italy', 'Germany', 'Belgium', 'Norway', 'US', 'United Kingdom', 'Korea, South',
  'China', 'Japan', 'Switzerland', 'Sweden', 'Denmark', 'Netherlands', 'Iran', 'Canada')
for(i in 1:length(countries)){
  roll_curve_country(the_country = countries[i])
}

Error in ts(x): 'ts' object must have one or more observations

# Cases
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data)

# Cases adjusted
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, pop = T)

# Deaths
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, deaths = T)

# Cases adjusted
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, pop = T, deaths = T)

plot_data <- esp_df  %>% mutate(geo = ccaa)

roll_curve(plot_data, pop = T, deaths = T)

plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)

roll_curve(plot_data, deaths = T)

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths / sum(deaths) * 100)
text_size <- 12

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = deaths)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths | Muertes',
       title = 'COVID-19 deaths in Spain',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(deaths > 0),
            aes(x = ccaa,
                y = deaths,
                label = paste0(deaths, '\n(',
                               round(p, digits = 1), '%)')),
            size = text_size * 0.3,
            nudge_y = 180) +
  ylim(0, max(pd$deaths * 1.1))

ggsave('/tmp/spain.png')

Muertes relativas por CCAA

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths / sum(deaths) * 100)

pd <- pd %>% left_join(esp_pop)
text_size <- 12
pd$value <- pd$deaths / pd$pop * 100000

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths per 100,000',
       title = 'COVID-19 deaths per 100.000',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(value > 0),
            aes(x = ccaa,
                y = value,
                label = paste0(round(value, digits = 2), '\n(',
                               deaths, '\ndeaths)')),
            size = text_size * 0.3,
            nudge_y = 4.5) +
  ylim(0, max(pd$value) * 1.2)

ggsave('/tmp/spai2.png')

Just yesterday

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths_non_cum / sum(deaths_non_cum) * 100)
text_size <- 12

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = deaths_non_cum)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths',
       title = 'COVID-19 deaths in Spain',
       subtitle = paste0('Data only for ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(deaths_non_cum > 0),
            aes(x = ccaa,
                y = deaths_non_cum,
                label = paste0(deaths_non_cum, '\n(',
                               round(p, digits = 1), '%)')),
            size = text_size * 0.3,
            nudge_y = 30) +
  ylim(0, max(pd$deaths_non_cum * 1.1))

ggsave('/tmp/spain_non_cum.png')

Muertes relativas por CCAA ayer SOLO

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths_non_cum / sum(deaths_non_cum) * 100)

pd <- pd %>% left_join(esp_pop)
text_size <- 12
pd$value <- pd$deaths_non_cum / pd$pop * 100000

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths per 100,000',
       title = 'COVID-19 deaths per 100.000',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(value > 0),
            aes(x = ccaa,
                y = value,
                label = paste0(round(value, digits = 2), '\n(',
                               deaths_non_cum, '\ndeaths)')),
            size = text_size * 0.3,
            nudge_y = 1) +
  ylim(0, max(pd$value) * 1.3)

ggsave('/tmp/spain_ayer_adj.png')

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, scales = 'fixed')

ggsave('/tmp/a.png',
       width = 1280 / 150,
       height = 720 / 150)

Loop for everywhere (see desktop)

dir.create('/tmp/ccaas')
ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, scales = 'fixed')  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_cases.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, scales = 'fixed', pop = TRUE)  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_cases_pop.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

# Deaths too
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, deaths = T, scales = 'fixed') + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_deaths.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

# Deaths too
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, deaths = T, scales = 'fixed', pop = TRUE)  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_deaths_pop.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, scales = 'free_y')

ggsave('/tmp/b.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, deaths = T, scales = 'free_y')

ggsave('/tmp/c.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, deaths = T, scales = 'fixed')

ggsave('/tmp/d.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- df_country %>% filter(country %in% c('Spain', 'Italy', 'Germany', 'France', 'US',
                                                  'China', 'Korea, South', 'Japan', 'Singapore')) %>% mutate(geo = country)
roll_curve(plot_data, scales = 'free_y')

ggsave('/tmp/z.png',
       width = 1280 / 150,
       height = 720 / 150)

World at large

pd <- df_country %>%
  group_by(date) %>%
  summarise(n = sum(cases)) %>%
  filter(date < max(date))
ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases')

ggsave('~/Videos/update/a.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/a.png'

China vs world

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(cases))  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n,
           color = country)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme(legend.text = element_text(size = 25),
        legend.position = 'top')

Error in FUN(X[[i]], ...): object 'country' not found

ggsave('~/Videos/update/b.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/b.png'

NOn china only

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(cases)) %>%
  filter(country == 'Other countries')  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases, outside of China') 

ggsave('~/Videos/update/c.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/c.png'

Case numbers across countries

plot_day_zero(countries = c('France', 'Germany', 'Italy', 'Spain', 'Switzerland', 'Sweden', 'Norway', 'Netherlands'))

# ggsave('~/Videos/update/d.png',
#        width = 1280 / 150,
#        height = 720 / 150)

World at large - deaths

pd <- df_country %>%
  group_by(date) %>%
  summarise(n = sum(deaths)) %>%
  filter(date < max(date))
ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Deaths',
       title = 'COVID-19 deaths')

# ggsave('~/Videos/update/e.png',
#        width = 1280 / 150,
#        height = 720 / 150)

China vs world deaths

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(deaths))  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n,
           color = country)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Deaths',
       title = 'COVID-19 deaths') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme(legend.text = element_text(size = 25),
        legend.position = 'top')

Error in FUN(X[[i]], ...): object 'country' not found

# ggsave('~/Videos/update/f.png',
#        width = 1280 / 150,
#        height = 720 / 150)

Asian hope

plot_day_zero(countries = c('Korea, South', 'Japan', 'China', 'Singapore'))

# ggsave('~/Videos/update/g.png',
#        width = 1280 / 150,
#        height = 720 / 150)

Since trajectories are very unstable when cases are low, we’ll exclude from our analysis the first few days, and will only count as “outbreak” once a country reaches 150 or more cumulative cases.

# Doubling time
n_cases_start = 150
countries = c('Italy', 'Spain', 'France', 'Germany', 'Italy', 'Switzerland', 'Denmark', 'US', 'United Kingdom', 'Norway')
# countries <- sort(unique(df_country$country))
out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <-df_country %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$cases, na.rm = TRUE) >= n_cases_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(cases)) %>%
      mutate(start_date = min(date[cases >= n_cases_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(cases) ~ days_since, data = pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(pd$days_since) + 5, by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, cases, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt,
                  slope = curve)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}
done <- bind_rows(out_list)
print(done)

# A tibble: 10 x 3
   country        doubling_time  slope
   <chr>                  <dbl>  <dbl>
 1 Italy                   5.28 0.131 
 2 Spain                   4.06 0.171 
 3 France                  4.39 0.158 
 4 Germany                 4.26 0.163 
 5 Italy                   5.28 0.131 
 6 Switzerland             5.34 0.130 
 7 Denmark                 8.50 0.0816
 8 US                      3.16 0.219 
 9 United Kingdom          3.96 0.175 
10 Norway                  8.01 0.0865

curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)

# Get rid of Italy future (since it's the "leader")
joined <- joined %>%
  filter(country != 'Italy' |
           date <= (Sys.Date() -1))


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, cases, predicted, doubling_time) %>%
  tidyr::gather(key, value, cases:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

The below chart shows the trajectories in terms of number of cases in Europe in red, and the predicted trajectories in black. The black line assumes that the doubling rate will stay constant.

cols <- c('red', 'black')
ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = long %>% filter(key != 'Confirmed cases'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = long %>% filter(key == 'Confirmed cases')) +
  geom_line(data = long %>% filter(key == 'Confirmed cases'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Cases',
       title = 'COVID-19 CASES: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >150 cumulative cases)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

Since Italy is “leading the way”, it’s helpful to also compare each country to Italy. Let’s see that.

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = cases) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, cases, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, cases: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = ol %>% filter(!key %in% c('Confirmed cases', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Confirmed cases')) +
  geom_line(data = ol %>% filter(key == 'Confirmed cases'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Cases',
       title = 'COVID-19 CASES: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >150 cumulative cases)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

In the above, what’s striking is how many places have trajectories that are worse than Italy’s. Yes, Italy has more cases, but it’s doubling time is less. Either that changes soon, or these other countries will soon have more cases than Italy.

Deaths or cases?

The number of cases is not necessarily the best indicator for the severity of an outbreak of this nature. Why? Because (a) testing rates and protocols are different by place and (b) testing rates are different by time (since health services are changing their approaches as things develop). In other words, when we compare the number of cases by place and time, we are introducing significant bias.

Using deaths to gauge the magnitude of the outbreak is also problematic. Death rates are differential by age, so the number of deaths depends on a country’s population period, or age structure. Also, death rates will be a function of health services, which are not of the same quality every where. And, of course, like cases, we don’t necessarily know about all of the deaths that occur because of COVID-19.

Still, there’s an argument that death rates have less bias than case rates because deaths are easier to identify than cases. Let’s accept that argument, for the time being, and have a look at death rates by country.

# Doubling time
n_deaths_start = 5
countries = c('Italy', 'Spain', 'France', 'Italy', 'Switzerland', 'Denmark', 'US', 'United Kingdom', 'Norway', 'Germany')
# countries <- sort(unique(df_country$country))

make_double_time <- function(data = df_country,
                             the_country = 'Spain',
                             n_deaths_start = 5,
                             time_ahead = 7){
   sub_data <-data %>% filter(country == the_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since)) %>%
      mutate(the_weight = 1/(1 + (as.numeric(max(date) - date))))
    fit <- lm(log(deaths) ~ days_since,
              weights = the_weight,
              data = pd) 
    # fitlo <- loess(deaths ~ days_since, data = pd)
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    day0 <- pd$date[pd$days_since == 0]
    fake <- tibble(days_since = seq(0, max(pd$days_since) + time_ahead, by = 1))
    fake <- fake %>%mutate(date = seq(day0, day0+max(fake$days_since), by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    # fake$predictedlo <- predict(fitlo, newdata = fake)
    ci <- exp(predict(fit, newdata = fake, interval = 'prediction'))
    # cilo <- predict(fitlo, newdata = fake, interval = 'prediction')

    fake$lwr <- ci[,'lwr']
    fake$upr <- ci[,'upr']
    # fake$lwrlo <- ci[,'lwr']
    # fake$uprlo <- ci[,'upr']
    # Doubling time
    dt <- log(2)/fit$coef[2]
    fake %>% mutate(country = the_country) %>% mutate(doubling_time = dt)
  }
}

plot_double_time <- function(data, ylog = F){
  the_labs <- labs(x = 'Date',
                   y = 'Deaths',
                   title = paste0('Predicted deaths in ', data$country[1]))
  long <- data %>%
    tidyr::gather(key, value, deaths:predicted) %>%
    mutate(key = Hmisc::capitalize(key))
  g <- ggplot() +
        geom_ribbon(data = data %>% filter(date > max(long$date[!is.na(long$value) & long$key == 'Deaths'])),
                aes(x = date,
                    ymax = upr,
                    ymin = lwr),
                alpha =0.6,
                fill = 'darkorange') +
    geom_line(data = long,
              aes(x = date,
                  y = value,
                  group = key,
                  lty = key)) +
    geom_point(data = long %>% filter(key == 'Deaths'),
               aes(x = date,
                   y = value)) +
    theme_simple() +
    theme(legend.position = 'right',
          legend.title = element_blank()) +
    the_labs
  if(ylog){
    g <- g + scale_y_log10()
  }
  return(g)
}
options(scipen = '999')
data <- make_double_time(n_deaths_start = 150, time_ahead = 7)
data

# A tibble: 36 x 8
   days_since date       country deaths predicted   lwr   upr doubling_time
        <dbl> <date>     <chr>    <dbl>     <dbl> <dbl> <dbl>         <dbl>
 1          0 2020-03-15 Spain      288     1092.  718. 1661.          6.55
 2          1 2020-03-16 Spain      491     1214.  808. 1825.          6.55
 3          2 2020-03-17 Spain      598     1350.  909. 2004.          6.55
 4          3 2020-03-18 Spain      767     1500. 1022. 2203.          6.55
 5          4 2020-03-19 Spain     1002     1668. 1149. 2421.          6.55
 6          5 2020-03-20 Spain     1326     1854. 1291. 2662.          6.55
 7          6 2020-03-21 Spain     1720     2061. 1451. 2928.          6.55
 8          7 2020-03-22 Spain     2182     2291. 1629. 3221.          6.55
 9          8 2020-03-23 Spain     2696     2547. 1829. 3545.          6.55
10          9 2020-03-24 Spain     3434     2831. 2053. 3904.          6.55
# … with 26 more rows

dir.create('/tmp/ccaa_predictions')

plot_double_time(data, ylog = T) +
  labs(subtitle = 'Basic log-linear model weighted at (1 + (1/ days ago)),\nassuming no change in growth trajectory since first day at >150 deaths')

ggsave('/tmp/ccaa_predictions/spain.png')
# All ccaas
ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  message(i)
  this_ccaa <- ccaas[i]
  sub_data <- esp_df %>% mutate(country = ccaa) 
  try({
    data <- make_double_time(
    data = sub_data,
    the_country = this_ccaa,
    n_deaths_start = 5,
    time_ahead = 7)
  plot_double_time(data, ylog = T) +
  labs(subtitle = 'Basic log-linear model weighted at (1 + (1/ days ago)), assuming no change in growth trajectory since first day at >5 deaths')
  ggsave(paste0('/tmp/ccaa_predictions/',
                this_ccaa, '.png'),
         height = 4.9,
         width = 8.5)
  })

}

Error in UseMethod("gather_") : 
  no applicable method for 'gather_' applied to an object of class "NULL"

Error in UseMethod("gather_") : 
  no applicable method for 'gather_' applied to an object of class "NULL"

# all_countries <- sort(unique(df_country$country))
# for(i in 1:length(all_countries)){
#   this_country <- all_countries[i]
#   data <- make_double_time(the_country = this_country, n_deaths_start = 5)
#   if(!is.null(data)){
#     # print(this_country)
#     g <- plot_double_time(data, ylog = F) +
#   labs(subtitle = 'Basic log-linear model assuming no change in growth trajectory since first day at >5 deaths')
#     ggsave(paste0('/tmp/', this_country, '.png'), height = 5, width = 8)
#     print(data)
#   }
# }

out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <-df_country %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(deaths) ~ days_since, data = pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(pd$days_since) + 5, by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...): NA/NaN/Inf in 'y'

done <- bind_rows(out_list)
curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)

# Get rid of Italy future (since it's the "leader")
joined <- joined %>%
  filter(country != 'Italy' |
           date <= (Sys.Date() -1))


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, deaths, predicted, doubling_time) %>%
  tidyr::gather(key, value, deaths:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'black')
sub_data <-  long %>% filter(country != 'US')
ggplot(data = sub_data,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = sub_data %>% filter(key != 'Deaths'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = sub_data %>% filter(key == 'Deaths')) +
  geom_line(data = sub_data %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 cumulative deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

Let’s again overlay Italy.

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = deaths) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'blue', 'black')
sub_data <- ol %>% 
  filter(!(key == 'Predicted (based on current doubling time)' &
             country == 'Spain' &
             days_since > 13))
ggplot(data = sub_data,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = sub_data %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = sub_data %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  geom_point(data = sub_data %>% filter(key == 'Deaths')) +
  geom_line(data = sub_data %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  scale_y_log10() +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15)) 

Let’s look just at Spain

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy',
                         country == 'Spain')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = deaths) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', 
                      ifelse(key == 'Deaths', 'Spain', key)))

cols <- c('blue',  'black', 'red')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  # geom_point(data = ol %>% filter(key == 'Deaths')) +
    geom_point(data = ol %>% filter(country == 'Spain',
                                    key == 'Spain'), size = 4, alpha = 0.6) +

  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  # facet_wrap(~paste0(country, '\n',
  #                    '(doubling time: ', 
  #                    round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,1)) +
  scale_color_manual(name = '',
                     values = cols) +
  scale_y_log10() +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15),
          axis.title = element_text(size = 18))

The importance of lag

Things are changing very rapidly. And measures being taken by these countries will have an impact on the outbreak.

But it’s important to remember that there is a lag between when an intervention takes place and when its effect is notable. Because of the incubation period - the number of days between someone getting infected and becoming sick - what we do today won’t really have an effect until next weekend. And the clinical cases that present today are among people who got infected a week ago.

Disease control measures work. We can see that clearly in the case of Hubei, Wuhan, Iran, Japan. And they will work in Europe too. But because many of these measures were implemented very recently, we won’t likely see a major effect for at least a few more days.

In the mean time, it’s important to practice social distancing. Stay away from others to keep both you and others safe. Listen to Health Authorities. Take this very seriously.

Spain and Italy regions

# Madrid vs Lombardy deaths
n_death_start <- 5
pd <- esp_df %>%
  # filter(ccaa == 'Madrid') %>%
  dplyr::select(date, ccaa, cases, deaths) %>%
  bind_rows(ita %>%
              # filter(ccaa == 'Lombardia') %>%
              dplyr::select(date, ccaa, cases, deaths)) %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(first_n_death = min(date[deaths >= n_death_start])) %>%
  ungroup %>%
  mutate(days_since_n_deaths = date - first_n_death) %>%
  filter(is.finite(days_since_n_deaths))

pd$country <- pd$ccaa
pd$cases <- pd$cases
countries <- sort(unique(pd$country))
out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <- pd %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  # ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  ok <- length(which(sub_data$deaths >= n_deaths_start))
  if(ok){
    counter <- counter + 1
    sub_pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(deaths) ~ days_since, data = sub_pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(sub_pd$days_since) + 5, by = 1))
    fake <- left_join(fake, sub_pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}
done <- bind_rows(out_list)
curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, deaths, predicted, doubling_time) %>%
  tidyr::gather(key, value, deaths:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
long <- long %>% filter(!is.na(doubling_time))

text_size <- 12

cols <- c('red', 'black')
ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = long %>% filter(key != 'Deaths'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = long %>% filter(key == 'Deaths')) +
  geom_line(data = long %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_y_log10() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.5),
          plot.title = element_text(size = 15))

Let’s overlay Lombardy

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Lombardia')
ol2 <- joined %>% filter(country == 'Lombardia') %>% dplyr::rename(Lombardia = deaths) %>%
  dplyr::select(Lombardia, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Lombardia,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Lombardia) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
ol <- ol %>% filter(!is.na(doubling_time))

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  scale_y_log10() +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Lombardia')),
            size = 0.5, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Deaths')) +
  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.5),
          plot.title = element_text(size = 15))

Show only Spanish regions vs. Lombardy

text_size <- 14

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Lombardia')
ol2 <- joined %>% filter(country == 'Lombardia') %>% dplyr::rename(Lombardia = deaths) %>%
  dplyr::select(Lombardia, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Lombardia,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Lombardia) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
ol <- ol %>% filter(!is.na(doubling_time))

# Only Spain
ol <- ol %>% filter(country %in% esp_df$ccaa) %>%
  filter(!country %in% 'Aragón')

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  scale_y_log10() +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Lombardia')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Lombardia')),
            size = 0.5, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Deaths')) +
  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.6),
          plot.title = element_text(size = 15))

Same plot but overlayed

Same as above, but overlaid

text_size <-10

# cols <- c('red', 'black')
long <- long %>% filter(country %in% c('Lombardia',
                                       'Emilia Romagna') |
                          country %in% esp_df$ccaa) %>%
  filter(country != 'Aragón')
places <- sort(unique(long$country))

cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 7, 'Spectral'))(length(places))
cols[which(places == 'Madrid')] <- 'red'
cols[which(places == 'Cataluña')] <- 'purple'
cols[which(places == 'Lombardia')] <- 'darkorange'
cols[which(places == 'Emilia Romagna')] <- 'darkgreen'

long$key <- ifelse(long$key != 'Deaths', 'Predicted', long$key)
long$key <- ifelse(long$key == 'Predicted', 'Muertes\nprevistas',
                   'Muertes\nobservadas')


# Keep only Madrid, Lombardy, Emilia Romagna
long <- long %>%
  filter(country %in% c('Madrid',
                        'Lombardia',
                        'Emilia Romagna'))

ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = country)) +
  geom_point(data = long %>% filter(key == 'Muertes\nobservadas'), size = 2, alpha = 0.8) +
  geom_line(data = long %>% filter(key == 'Muertes\nprevistas'), size = 1, alpha = 0.7) +
  geom_line(data = long %>% filter(key != 'Muertes\nprevistas'), size = 0.8) +
  theme_simple() +
  scale_y_log10() +
  scale_linetype_manual(name ='',
                        values = c(1,4)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  # labs(x = 'Days since first day at 5 or more cumulative deaths',
  #      y = 'Deaths',
  #      title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
  #      caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
  #      subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    labs(x = 'Días desde el primer día a 5 o más muertes acumuladas',
       y = 'Muertes (escala logarítmica)',
       title = 'Muertes por COVID-19',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Tasa de crecimiento calculada desde el primer día a 5 o más muertes acumuladas)\n(Muertes "previstas": suponiendo que no hay cambios en la tasa de crecimiento)') +
    theme(strip.text = element_text(size = text_size * 0.75),
          plot.title = element_text(size = text_size * 3),
          legend.text = element_text(size = text_size * 1.5),
          axis.title = element_text(size = text_size * 2),
          axis.text = element_text(size = text_size * 2))

# cols <- c(cols, 'darkorange')
# ggplot(data = ol,
#        aes(x = days_since,
#            y = value,
#            lty = key,
#            color = key)) +
#   scale_y_log10() +
#   geom_line(aes(color = country)) +
#   
#   # geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
#   #           size = 1.2, alpha = 0.8) +
#   #   geom_line(data = ol %>% filter(key %in% c('Lombardia')),
#   #           size = 0.5, alpha = 0.8) +
#   # geom_point(data = ol %>% filter(key == 'Deaths')) +
#   # geom_line(data = ol %>% filter(key == 'Deaths'),
#   #           size = 0.8) +
#   theme_simple() +
#   scale_linetype_manual(name ='',
#                         values = c(1,6,2)) +
#   scale_color_manual(name = '',
#                      values = cols) +
#   theme(legend.position = 'top') +
#   labs(x = 'Days since first day at >5 deaths',
#        y = 'Deaths',
#        title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
#        caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
#        subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
#     theme(strip.text = element_text(size = text_size * 1),
#           plot.title = element_text(size = 15))

# Map data preparation

if(!'map.RData' %in% dir()){
  esp1 <- getData(name = 'GADM', country = 'ESP', level = 1)
# Remove canary
esp1 <- esp1[esp1@data$NAME_1 != 'Islas Canarias',]
espf <- fortify(esp1, region = 'NAME_1')

# Standardize names
# Convert names
map_names <- esp1@data$NAME_1
data_names <- sort(unique(esp_df$ccaa))
names_df <- tibble(NAME_1 = c('Andalucía',
 'Aragón',
 'Cantabria',
 'Castilla-La Mancha',
 'Castilla y León',
 'Cataluña',
 'Ceuta y Melilla',
 'Comunidad de Madrid',
 'Comunidad Foral de Navarra',
 'Comunidad Valenciana',
 'Extremadura',
 'Galicia',
 'Islas Baleares',
 'La Rioja',
 'País Vasco',
 'Principado de Asturias',
 'Región de Murcia'),
 ccaa = c('Andalucía',
 'Aragón',
 'Cantabria',
 'CLM',
 'CyL',
 'Cataluña',
 'Melilla',
 'Madrid',
 'Navarra',
 'C. Valenciana',
 'Extremadura',
 'Galicia',
 'Baleares',
 'La Rioja',
 'País Vasco',
 'Asturias',
 'Murcia'))


espf <- left_join(espf %>% dplyr::rename(NAME_1 = id), names_df)
centroids <- data.frame(coordinates(esp1))
names(centroids) <- c('long', 'lat')
centroids$NAME_1 <- esp1$NAME_1
centroids <- centroids %>% left_join(names_df)

# Get random sampling points

  random_list <- list()
  for(i in 1:nrow(esp1)){
    message(i)
    shp <- esp1[i,]
    # bb <- bbox(shp)
    this_ccaa <- esp1@data$NAME_1[i]
    # xs <- runif(n = 500, min = bb[1,1], max = bb[1,2])
    # ys <- runif(n = 500, min = bb[2,1], max = bb[2,2])
    # random_points <- expand.grid(long = xs, lat = ys) %>%
    #   mutate(x = long,
    #          y = lat)
    # coordinates(random_points) <- ~x+y
    # proj4string(random_points) <- proj4string(shp)
    # get ccaa
    message('getting locations of randomly generated points')
    # polys <- over(random_points,polygons(shp))
    # polys <- as.numeric(polys)
    random_points <- spsample(shp, n = 20000, type = 'random')
    random_points <- data.frame(random_points)
    random_points$NAME_1 <-  this_ccaa
    random_points <- left_join(random_points, names_df) %>% dplyr::select(-NAME_1)
    random_list[[i]] <- random_points
  }
  random_points <- bind_rows(random_list)
  random_points <- random_points %>% mutate(long = x,
                                            lat = y)

save(espf,
     esp1,
     names_df,
     centroids,
     random_points,
     file = 'map.RData')
} else {
  load('map.RData')
}

# Define a function for adding zerio
add_zero <- 
  function (x, n) 
  {
    x <- as.character(x)
    adders <- n - nchar(x)
    adders <- ifelse(adders < 0, 0, adders)
    for (i in 1:length(x)) {
      if (!is.na(x[i])) {
        x[i] <- paste0(paste0(rep("0", adders[i]), collapse = ""), 
                       x[i], collapse = "")
      }
    }
    return(x)
  }

remake_world_map <- FALSE
options(scipen = '999')
if(remake_world_map){
  # World map animation
  world <- map_data('world')
  # world <- ne_countries(scale = "medium", returnclass = "sf")
  
  # Get plotting data
  pd <- df_country %>%
    dplyr::select(date, lng, lat, n = cases)
  dates <- sort(unique(pd$date))
  n_days <- length(dates)
  # # Define vectors for projection
  # vec_lon <- seq(30, -20, length = n_days)
  # vec_lat <- seq(25, 15, length = n_days)
  
  dir.create('animation')
  for(i in 1:n_days){
    message(i, ' of ', n_days)
    this_date <- dates[i]
    # this_lon <- vec_lon[i]
    # this_lat <- vec_lat[i]
    # the_crs <-
    #   paste0("+proj=laea +lat_0=", this_lat,
    #          " +lon_0=",
    #          this_lon,
    #          " +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs ")
    sub_data <- pd %>%
      filter(date == this_date)
    # coordinates(sub_data) <- ~lng+lat
    # proj4string(sub_data) <- proj4string(esp1)
    # # sub_data <- spTransform(sub_data,
    # #                         the_crs)
    # coordy <- coordinates(sub_data)
    # sub_data@data$long <- coordy[,1]
    # sub_data@data$lat <- coordy[,2]
  
    g <- ggplot() +
      geom_polygon(data = world,
                   aes(x = long,
                       y = lat,
                       group = group),
                   fill = 'black',
                   color = 'white',
                   size = 0.1) +
      theme_map() +
          geom_point(data = sub_data %>% filter(n > 0) %>% mutate(Deaths = n),
                 aes(x = lng,
                     y = lat,
                     size = Deaths),
                 color = 'red',
                 alpha = 0.6) +
      geom_point(data = tibble(x = c(0,0), y = c(0,0), Deaths = c(1, 100000)),
                 aes(x = x,
                     y = y,
                     size = Deaths),
                 color = 'red',
                 alpha =0.001) +
      scale_size_area(name = '', breaks = c(100, 1000, 10000, 100000),
                      max_size = 25
                      ) +
    # scale_size_area(name = '', limits = c(1, 10), breaks = c(0, 10, 30, 50, 70, 100, 200, 500)) +
      labs(title = this_date) +
      theme(plot.title = element_text(size = 30),
            legend.text = element_text(size = 15),
            legend.position = 'left')
  
    plot_number <- add_zero(i, 3)
    ggsave(filename = paste0('animation/', plot_number, '.png'),
           plot = g,
           width = 9.5,
           height = 5.1)
  }
  setwd('animation')
  system('convert -delay 30x100 -loop 0 *.png result.gif')
  setwd('..')

}

Maps of Spain

make_map <- function(var = 'deaths',
                     data = NULL,
                     pop = FALSE,
                     pop_factor = 100000,
                     points = FALSE,
                     line_color = 'white',
                     add_names = T,
                     add_values = T,
                     text_size = 2.7){
  
  if(is.null(data)){
    data <- esp_df %>%  mutate(ccaa = cat_transform(ccaa))

  }

  left <- espf %>%   mutate(ccaa = cat_transform(ccaa)) 
  right <- data[,c('ccaa', paste0(var, '_non_cum'))]
  

  names(right)[ncol(right)] <- 'var'
  right <- right %>% group_by(ccaa) %>% summarise(var = sum(var, na.rm = T))
  
  if(pop){
    right <- left_join(right, esp_pop)
    right$var <- right$var / right$pop * pop_factor
  }
  map <- left_join(left, right)
  
  if(points){
    the_points <- centroids %>%
      left_join(right)
    g <- ggplot() +
      geom_polygon(data = map,
         aes(x = long,
             y = lat,
             group = group),
         fill = 'black',
         color = line_color,
         lwd = 0.4, alpha = 0.8) +
      geom_point(data = the_points,
                 aes(x = long,
                     y = lat,
                     size = var),
                 color = 'red',
                 alpha = 0.7) +
      scale_size_area(name = '', max_size = 20)
  } else {
    # cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
    cols <- RColorBrewer::brewer.pal(n = 8, name = 'Blues')
    g <- ggplot(data = map,
         aes(x = long,
             y = lat,
             group = group)) +
    geom_polygon(aes(fill = var),
                 lwd = 0.3,
                 color = line_color) +
      scale_fill_gradientn(name = '',
                           colours = cols)
    # scale_fill_viridis(name = '' ,option = 'magma',
    #                    direction = -1) 
  }
  
  # Add names?
  if(add_names){
    centy <- centroids %>% left_join(right)
    if(add_values){
      centy$label <- paste0(centy$ccaa, '\n(', round(centy$var, digits = 2), ')')
    } else {
      centy$label <- centy$ccaa
    }

    g <- g +
      geom_text(data = centy,
                aes(x = long,
                    y = lat,
                    label = label,
                    group = ccaa),
                alpha = 0.7,
                size = text_size)
  }
  
  g +
    theme_map() +
    labs(subtitle = paste0('Data as of ', max(data$date))) +
    theme(legend.position = 'right')
  
}

make_dot_map <- function(var = 'deaths',
                     date = NULL,
                     pop = FALSE,
                     pop_factor = 100,
                     point_factor = 1,
                     points = FALSE,
                     point_color = 'darkred',
                     point_size = 0.6,
                     point_alpha = 0.5){
  
  
  if(is.null(date)){
    the_date <- max(esp_df$date)
  } else {
    the_date <- date
  }
    right <- esp_df[esp_df$date == the_date,c('ccaa', var)]
   names(right)[ncol(right)] <- 'var'
  if(pop){
    right <- left_join(right, esp_pop)
    right$var <- right$var / right$pop * pop_factor
  }
  map_data <- esp1@data %>%
    left_join(names_df) %>%
      left_join(right)
  map_data$var <- map_data$var / point_factor
  out_list <- list()
  for(i in 1:nrow(map_data)){
    sub_data <- map_data[i,]
    this_value = round(sub_data$var)

    if(this_value >= 1){
      this_ccaa = sub_data$ccaa
      # get some points
      sub_points <- random_points %>% filter(ccaa == this_ccaa)
      sampled_points <- sub_points %>% dplyr::sample_n(this_value)
      out_list[[i]] <- sampled_points
    }
  }
  the_points <- bind_rows(out_list)
  
  g <- ggplot() +
    geom_polygon(data = espf,
         aes(x = long,
             y = lat,
             group = group),
         fill = 'white',
         color = 'black',
         lwd = 0.4, alpha = 0.8) +
    geom_point(data = the_points,
               aes(x = long,
                   y = lat),
               color = point_color,
               size = point_size,
               alpha = point_alpha)
  g +
    theme_map() +
    labs(subtitle = paste0('Data as of ', max(esp_df$date)))
  
}

Deaths

Absolute number of deaths: points

make_map(var = 'deaths',
       points = T) +
  labs(title = 'Number of deaths',
       caption = '@joethebrew')

Absolute number of deaths: choropleth

make_map(var = 'deaths',
         line_color = 'darkgrey',
       points = F) +
  labs(title = 'Number of deaths',
       caption = '@joethebrew')

Number of deaths adjusted by population: points

make_map(var = 'deaths', pop = TRUE, points = T) +
  labs(title = 'Number of deaths per 100,000',
       caption = '@joethebrew')

Number of deaths adjusted by population: polygons

make_map(var = 'deaths', pop = TRUE, points = F, line_color = 'darkgrey') +
  labs(title = 'Number of deaths per 100,000',
       caption = '@joethebrew')

Number of deaths: 1 dot per death

make_dot_map(var = 'deaths', point_size = 0.05) +
  labs(title = 'COVID-19 deaths: 1 point = 1 death\nImportant: points are random within each CCAA; do not reflect exact location',
       caption = '@joethebrew')

Cases

Absolute number of cases: points

make_map(var = 'cases',
       points = T) +
  labs(title = 'Number of confirmed cases',
       caption = '@joethebrew')

Absolute number of cases: choropleth

make_map(var = 'cases',
         line_color = 'darkgrey',
       points = F) +
  labs(title = 'Number of confirmed cases',
       caption = '@joethebrew')

Number of cases adjusted by population: points

make_map(var = 'cases', pop = TRUE, points = T) +
  labs(title = 'Number of confirmed cases per 100,000',
       caption = '@joethebrew')

Number of cases adjusted by population: polygons

make_map(var = 'cases', pop = TRUE, points = F,
         line_color = 'darkgrey') +
  labs(title = 'Number of confirmed cases per 100,000',
       caption = '@joethebrew')

Number of cases: points

make_dot_map(var = 'cases',
             point_size = 0.05, point_alpha = 0.5, point_factor = 10) +
  labs(title = 'COVID-19 cases: 1 point = 10 cases\nImportant: points are random within each CCAA; do not reflect exact location',
       caption = '@joethebrew')